Data Pipelines and Causal Inference in Machine Learning

Join us for our first Meetup in Berlin.
Presented by QuantumBlack, a McKinsey Company

Abstract:

Streamlining Machine Learning Workflows
by Aris Valtazanos, PhD | Head of Machine Learning Engineering / Senior Principal Data Scientist at QuantumBlack
Data scientists often find themselves rewriting code from scratch, even when working on problems they have previously encountered. In this talk, we will discuss why reusable code matters, and how it can lead to more productive data science teams. We will first give an introduction to Kedro, an open-source Python library developed by QuantumBlack, which helps data scientists structure and manage their workflows. We will then review other analytics themes we frequently come across, and discuss our process of building reusable code libraries around them. This process will be illustrated through a deeper dive on Causal Inference in the second talk.

Causal Inference
by Nisara Sriwattanaworachai, PhD | Data Scientist at QuantumBlack
More and more organisations are using machine learning to help make business decisions. In this type of problem it is crucial to distinguish between events that cause outcomes and those that merely correlate. This talk will discuss the concept of Bayesian networks for modelling causality. We will cover what Bayesian networks are, how they are constructed from data and domain expertise, and how we use them to generate deeper insights for our clients.

Agenda:
6:45pm - Arrival & Finger Food
7:15pm - Streamlining ML Workflows
7:45pm - Causal Inference
8:15pm - Q&A and Networking

This event is sponsored by QuantumBlack

QuantumBlack is an advanced analytics firm operating at the intersection of strategy, technology and design to improve performance outcomes for organisations.

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